Snake Optimization Algorithm for Variable Selection in Spectral Quantitative Analysis of Complex Samples

ABSTRACT

The discretized snake optimization algorithm was first proposed as a variable selection method to reduce irrelevant variables and enhance the prediction accuracy of complex samples. In discretized snake optimization (SO), the positions of the snakes were updated, and three transfer functions, V‐shaped, arctangent and sigmoid functions, were introduced and compared for discretization. The partial least squares (PLS) model was built using the spectral variables selected by discretized SO. The performance of snake population, transfer functions in SO, and distribution of selected variables for different methods are investigated. To verify the feasibility of SO‐PLS, the predictive accuracy of SO‐PLS was compared with full‐spectrum PLS, uninformative variable elimination‐PLS (UVE‐PLS), Monte Carlo UVE‐PLS (MCUVE‐PLS), randomization test‐PLS (RT‐PLS), grey wolf optimizer‐PLS (GWO‐PLS), and whale optimization algorithm‐PLS (WOA‐PLS) models on four complex sample datasets. The results indicate that the V‐shaped function is the best transfer function. Compared with the other variable selection methods, SO‐PLS uses the least number of variables and gets the best prediction accuracy. Furthermore, compared to PLS, the SO‐PLS model reduced the root mean squared error of prediction (RMSEP) by 52%, 43%, 38%, and 23% for predicting protein, sugar, alcohol, and fat in wheat, orange juice, wine, and cocoa bean dataset, respectively. The conresponding correlation coefficients ( R ) increased from 0.8942, 0.7375, 0.9984, and 0.8121 to 0.9782, 0.8935, 0.9996, and 0.8920, respectively.